class_docstr: The ``KeypointDetector`` is a :class:`~flash.Task` for detecting keypoints in images. For more details, see
  :ref:`keypoint_detection`.
  
  Args:
      num_keypoints: Number of keypoints to detect.
      num_classes: The number of keypoint classes.
      backbone: String indicating the backbone CNN architecture to use.
      head: String indicating the head module to use on top of the backbone.
      pretrained: Whether the model should be loaded with it's pretrained weights.
      optimizer: Optimizer to use for training.
      lr_scheduler: The LR scheduler to use during training.
      learning_rate: The learning rate to use for training.
      predict_kwargs: dictionary containing parameters that will be used during the prediction phase.
      **kwargs: additional kwargs used for initializing the task
code: def predict_kwargs(self, predict_kwargs: Dict[str, Any]):
      self.adapter.predict_kwargs = predict_kwargs
  
file_path: lightning-flash/src/flash/image/keypoint_detection/model.py
imports: from typing import Any, Dict, List, Optional
  from flash.core.adapter import AdapterTask
  from flash.core.registry import FlashRegistry
  from flash.core.utilities.types import LR_SCHEDULER_TYPE, OPTIMIZER_TYPE
  from flash.image.keypoint_detection.backbones import KEYPOINT_DETECTION_HEADS
label_desc: Use image node_id 4 for calling the KeypointDetector obj's underlying member method code with example usage: obj.predict_kwargs(predict_kwargs) without return types